Dealing With Real-World Data

This course covers a subject central to the practice of data science and machine learning: the tricky and often overlooked problem of how to deal with real-world data. It provides an overview of the things data scientists think about when gaining access to a data set. You'll learn about data types, data exploration, the curse of dimensionality, PCA, model evaluation, and more, in this pragmatic introduction to the terminology and concepts surrounding data and machine learning. Learners with a basic working knowledge of mathematics will be able to enjoy the course and immediately start working on machine learning problems.

• Learn to handle the many types of data used in real-world machine learning projects
• Explore topics like data exploration, the curse of dimensionality, and PCA
• Understand how to evaluate models and why this is important
• Learn how to use — and enjoy free access to — the SherlockML data science platform
• Develop the skills required for the machine learning job market where demand outstrips supply

Angie Ma, Gary Willis, and Alessandra Stagliano are data scientists with ASI Data Science, a London based AI/machine learning solutions firm. Angie co-founded ASI and is also the founder of Data Science Lab London, one of the biggest communities of data scientists and data engineers in Europe, with over 2,500 members. Angie holds a PhD in physics from London's University College, Gary Willis holds a PhD in statistical physics from London's Imperial College, and Alessandra Stagliano holds a PhD in computer science from the University of Genoa. Collectively, the group has worked on over 150 commercial AI/machine learning projects.